Day 1: Getting data into shape

Tidyverse world: 1) workflow: 2) Rstudio: 3) importing data: 4) manipulating data

Day’s objectives

1. Understand workflow, why it is important, and how to do it using Rstudio/R
2. Have an understanding of the tidyverse framework and its key packages
3. Work through an example

Key packages

Example of some workflows

mine

Paul’s

Commonalities

Advantages of workflow

asfd

adsf

Dataset for workshop

For this workshop, we will you an example dataset to go through the whole workflow process.

Rstudio

You can find a variety of cheatsheets at https://posit.co/resources/cheatsheets/

See Rstudio IDE to Positron https://github.com/posit-dev/positron/wiki for note about new IDE

Basics

• assume familiar with
• might want to turn off .Rdata so everything is fresh on startup (no issues with previous objects so completely reproducible)

Tips and tricks

• tab
• fills in paths
• hexcode color coating
• code folding (#)
• insert pipe: ctrl+shift+M %>%
• comment: ctrl + shift + C
• find in all files

Programming good technique

• consistent style
• modularize your code (functions - slightly more advanced)

Tidyverse framework

Historical context

• quirky things about R: factors vs character, NAs,

• tibble is data.frame v2.0

  • better printing of the data
  • handles some of the quirky things that caught up people
  • column names

• lubridate fixed date quirks

Setting up Rproject

Rproject

Project directory/folder structure

Hands-on

Your Task Setup an Rproject for the workshop study

• Call the Rproject “projectDADA”
• Create subfolders: data/, R/

Importing

Saving the data files

Excel files

Key functions

Show code

  readxl::read_xls()
  readxl::read_xlsx()

Tips and tricks

• NAs - defining

• rename columns

• reading in more rows to get column type correct

• setup excel file to have NA

CSV/Table

Show code

  readr::read_csv()
  readr::read_table()

Advantages

SEtting your column types

Hands-on

After setting up the folder structure for your project, you need to add your data. Here, you will download the data/ directory using link below:

Download data folder for project

After the zip file downloads, unzip and drag the data/ folder to your Rproject folder.

XXXX data/raw, data/rds, data/spatial XXXX

Your Task

Import your data

• Call the Rproject projectDADA
• Create subfolders: data/, R/
• Create a new r script calls importData.r and save in R/ folder

Data cleaning

Joins

Show code

  knitr::include_graphics("lubridate.pdf")

knitr::include_graphics(“dplyr.pdf”)

Note: joins = merges (synonyms)

Tips and tricks

• Essential to make sure that the number of rows out matches your expectation
• Almost all my joins are

Your Task

Restructuring

Show code

  knitr::include_graphics("lubridate.pdf")

knitr::include_graphics(“tidyr.pdf”)

Your Task

String manipulation

Show code

  knitr::include_graphics("lubridate.pdf")

knitr::include_graphics(“stringr.pdf”)

Your Task

Date/time

Show code

  knitr::include_graphics("lubridate.pdf")

Your Task

Saving the clean data

At the end of the import and clean stage, I save the dataset as a